Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System

Diagnosis and prediction of the health status of vehicle components production line machine is the core requirement for global manufacturing system. With the development of Internet of things (IoT), there are enormous big data of production line could be collected quickly and stored in large quantities. The development of artificial intelligence makes it possible to deal with big data efficiently. Due to the industrial requirement of health self-diagnosis for vehicle production line, this paper presents a method based on Artificial Neural Network (ANN) to diagnose the health status of production line machines using the data produced by the machines. The PID control parameters of motors are segmented to simulate the health status of the machines in a long duration. We use three kinds of artificial neural network (ANN) methods to train the model of the relationship between the large data trend and the diagnostic score of the machine, it is demonstrated that it becomes more efficient than traditional empirical analysis to improve the speed and accuracy for diagnostic and prediction of machines health status.